Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations4600
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory575.1 KiB
Average record size in memory128.0 B

Variable types

DateTime1
Numeric12
Categorical3

Alerts

Age is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
Total_sqft is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
bathrooms is highly overall correlated with Age and 6 other fieldsHigh correlation
bedrooms is highly overall correlated with Total_sqft and 3 other fieldsHigh correlation
floors is highly overall correlated with Age and 3 other fieldsHigh correlation
price is highly overall correlated with Total_sqft and 2 other fieldsHigh correlation
sqft_above is highly overall correlated with Total_sqft and 5 other fieldsHigh correlation
sqft_basement is highly overall correlated with Total_sqftHigh correlation
sqft_living is highly overall correlated with Total_sqft and 4 other fieldsHigh correlation
yr_built is highly overall correlated with Age and 2 other fieldsHigh correlation
waterfront is highly imbalanced (93.9%) Imbalance
view is highly imbalanced (71.9%) Imbalance
price is highly skewed (γ1 = 24.79093256) Skewed
price has 49 (1.1%) zeros Zeros
sqft_basement has 2745 (59.7%) zeros Zeros
yr_renovated has 2735 (59.5%) zeros Zeros

Reproduction

Analysis started2024-12-01 14:18:17.419360
Analysis finished2024-12-01 14:18:36.319357
Duration18.9 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

date
Date

Distinct70
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
Minimum2014-05-02 00:00:00
Maximum2014-07-10 00:00:00
2024-12-01T15:18:36.402988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:36.571597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1741
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551962.99
Minimum0
Maximum26590000
Zeros49
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:36.742748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200000
Q1322875
median460943.46
Q3654962.5
95-th percentile1184050
Maximum26590000
Range26590000
Interquartile range (IQR)332087.5

Descriptive statistics

Standard deviation563834.7
Coefficient of variation (CV)1.0215082
Kurtosis1044.3522
Mean551962.99
Median Absolute Deviation (MAD)157500
Skewness24.790933
Sum2.5390297 × 109
Variance3.1790957 × 1011
MonotonicityNot monotonic
2024-12-01T15:18:36.913009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
1.1%
300000 42
 
0.9%
400000 31
 
0.7%
600000 29
 
0.6%
450000 29
 
0.6%
440000 29
 
0.6%
350000 28
 
0.6%
250000 27
 
0.6%
550000 27
 
0.6%
415000 27
 
0.6%
Other values (1731) 4282
93.1%
ValueCountFrequency (%)
0 49
1.1%
7800 1
 
< 0.1%
80000 1
 
< 0.1%
83000 1
 
< 0.1%
83300 2
 
< 0.1%
84350 1
 
< 0.1%
87500 1
 
< 0.1%
90000 2
 
< 0.1%
100000 4
 
0.1%
102500 1
 
< 0.1%
ValueCountFrequency (%)
26590000 1
< 0.1%
12899000 1
< 0.1%
7062500 1
< 0.1%
4668000 1
< 0.1%
4489000 1
< 0.1%
3800000 1
< 0.1%
3710000 1
< 0.1%
3200000 1
< 0.1%
3100000 1
< 0.1%
3000000 1
< 0.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4008696
Minimum0
Maximum9
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:37.048631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90884812
Coefficient of variation (CV)0.26723992
Kurtosis1.2353774
Mean3.4008696
Median Absolute Deviation (MAD)1
Skewness0.45644663
Sum15644
Variance0.8260049
MonotonicityNot monotonic
2024-12-01T15:18:37.161264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 2032
44.2%
4 1531
33.3%
2 566
 
12.3%
5 353
 
7.7%
6 61
 
1.3%
1 38
 
0.8%
7 14
 
0.3%
8 2
 
< 0.1%
0 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 38
 
0.8%
2 566
 
12.3%
3 2032
44.2%
4 1531
33.3%
5 353
 
7.7%
6 61
 
1.3%
7 14
 
0.3%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
< 0.1%
7 14
 
0.3%
6 61
 
1.3%
5 353
 
7.7%
4 1531
33.3%
3 2032
44.2%
2 566
 
12.3%
1 38
 
0.8%
0 2
 
< 0.1%

bathrooms
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1608152
Minimum0
Maximum8
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:37.286481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.78378107
Coefficient of variation (CV)0.36272471
Kurtosis1.8659047
Mean2.1608152
Median Absolute Deviation (MAD)0.5
Skewness0.61603272
Sum9939.75
Variance0.61431277
MonotonicityNot monotonic
2024-12-01T15:18:37.438034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2.5 1189
25.8%
1 743
16.2%
1.75 629
13.7%
2 427
 
9.3%
2.25 419
 
9.1%
1.5 291
 
6.3%
2.75 276
 
6.0%
3 167
 
3.6%
3.5 162
 
3.5%
3.25 136
 
3.0%
Other values (16) 161
 
3.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.75 17
 
0.4%
1 743
16.2%
1.25 3
 
0.1%
1.5 291
 
6.3%
1.75 629
13.7%
2 427
 
9.3%
2.25 419
 
9.1%
2.5 1189
25.8%
2.75 276
 
6.0%
ValueCountFrequency (%)
8 1
 
< 0.1%
6.75 1
 
< 0.1%
6.5 1
 
< 0.1%
6.25 2
 
< 0.1%
5.75 1
 
< 0.1%
5.5 4
 
0.1%
5.25 4
 
0.1%
5 6
 
0.1%
4.75 7
 
0.2%
4.5 29
0.6%

sqft_living
Real number (ℝ)

High correlation 

Distinct566
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2139.347
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:37.637183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile950
Q11460
median1980
Q32620
95-th percentile3870
Maximum13540
Range13170
Interquartile range (IQR)1160

Descriptive statistics

Standard deviation963.20692
Coefficient of variation (CV)0.45023408
Kurtosis8.2916826
Mean2139.347
Median Absolute Deviation (MAD)570
Skewness1.7235133
Sum9840996
Variance927767.56
MonotonicityNot monotonic
2024-12-01T15:18:37.820897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1940 32
 
0.7%
1720 32
 
0.7%
1840 31
 
0.7%
1660 31
 
0.7%
2000 30
 
0.7%
1410 29
 
0.6%
1200 28
 
0.6%
1480 28
 
0.6%
1890 27
 
0.6%
1490 27
 
0.6%
Other values (556) 4305
93.6%
ValueCountFrequency (%)
370 1
< 0.1%
380 1
< 0.1%
420 1
< 0.1%
430 1
< 0.1%
490 1
< 0.1%
520 1
< 0.1%
550 1
< 0.1%
560 1
< 0.1%
580 1
< 0.1%
590 2
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
10040 1
< 0.1%
9640 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
7320 1
< 0.1%
7270 1
< 0.1%
7050 1
< 0.1%
6980 1
< 0.1%
6900 1
< 0.1%

sqft_lot
Real number (ℝ)

Distinct3113
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14852.516
Minimum638
Maximum1074218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:37.995664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum638
5-th percentile1690.8
Q15000.75
median7683
Q311001.25
95-th percentile43560
Maximum1074218
Range1073580
Interquartile range (IQR)6000.5

Descriptive statistics

Standard deviation35884.436
Coefficient of variation (CV)2.416051
Kurtosis219.87299
Mean14852.516
Median Absolute Deviation (MAD)2772
Skewness11.307139
Sum68321574
Variance1.2876928 × 109
MonotonicityNot monotonic
2024-12-01T15:18:38.165266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 80
 
1.7%
6000 65
 
1.4%
4000 54
 
1.2%
7200 50
 
1.1%
4800 29
 
0.6%
9600 25
 
0.5%
4500 25
 
0.5%
7500 23
 
0.5%
5500 23
 
0.5%
3000 23
 
0.5%
Other values (3103) 4203
91.4%
ValueCountFrequency (%)
638 1
< 0.1%
681 1
< 0.1%
704 1
< 0.1%
746 1
< 0.1%
747 1
< 0.1%
750 1
< 0.1%
779 1
< 0.1%
833 1
< 0.1%
835 1
< 0.1%
844 2
< 0.1%
ValueCountFrequency (%)
1074218 1
< 0.1%
641203 1
< 0.1%
478288 1
< 0.1%
435600 2
< 0.1%
423838 1
< 0.1%
389126 1
< 0.1%
327135 1
< 0.1%
307752 1
< 0.1%
306848 1
< 0.1%
284011 1
< 0.1%

floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5120652
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:38.298440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53828838
Coefficient of variation (CV)0.35599548
Kurtosis-0.53885198
Mean1.5120652
Median Absolute Deviation (MAD)0.5
Skewness0.55144065
Sum6955.5
Variance0.28975438
MonotonicityNot monotonic
2024-12-01T15:18:38.418922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 2174
47.3%
2 1811
39.4%
1.5 444
 
9.7%
3 128
 
2.8%
2.5 41
 
0.9%
3.5 2
 
< 0.1%
ValueCountFrequency (%)
1 2174
47.3%
1.5 444
 
9.7%
2 1811
39.4%
2.5 41
 
0.9%
3 128
 
2.8%
3.5 2
 
< 0.1%
ValueCountFrequency (%)
3.5 2
 
< 0.1%
3 128
 
2.8%
2.5 41
 
0.9%
2 1811
39.4%
1.5 444
 
9.7%
1 2174
47.3%

waterfront
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
0
4567 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Length

2024-12-01T15:18:38.547486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T15:18:38.650111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

view
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
0
4140 
2
 
205
3
 
116
4
 
70
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4600
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row4
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Length

2024-12-01T15:18:38.758736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T15:18:38.870399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

condition
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
3
2875 
4
1252 
5
435 
2
 
32
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4600
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Length

2024-12-01T15:18:38.992611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T15:18:39.107113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

sqft_above
Real number (ℝ)

High correlation 

Distinct511
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1827.2654
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:39.254776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile860
Q11190
median1590
Q32300
95-th percentile3440
Maximum9410
Range9040
Interquartile range (IQR)1110

Descriptive statistics

Standard deviation862.16898
Coefficient of variation (CV)0.47183565
Kurtosis4.0701383
Mean1827.2654
Median Absolute Deviation (MAD)490
Skewness1.4942107
Sum8405421
Variance743335.34
MonotonicityNot monotonic
2024-12-01T15:18:39.415026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010 47
 
1.0%
1200 47
 
1.0%
1300 45
 
1.0%
1140 44
 
1.0%
1320 43
 
0.9%
1150 42
 
0.9%
1180 40
 
0.9%
1090 40
 
0.9%
1400 38
 
0.8%
1050 37
 
0.8%
Other values (501) 4177
90.8%
ValueCountFrequency (%)
370 1
 
< 0.1%
380 1
 
< 0.1%
420 1
 
< 0.1%
430 1
 
< 0.1%
490 1
 
< 0.1%
520 1
 
< 0.1%
550 3
0.1%
560 1
 
< 0.1%
580 1
 
< 0.1%
590 2
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8020 1
< 0.1%
7680 1
< 0.1%
7320 1
< 0.1%
6640 1
< 0.1%
6430 1
< 0.1%
6420 1
< 0.1%
6120 1
< 0.1%
6070 1
< 0.1%
6050 1
< 0.1%

sqft_basement
Real number (ℝ)

High correlation  Zeros 

Distinct207
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.08152
Minimum0
Maximum4820
Zeros2745
Zeros (%)59.7%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:39.573661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3610
95-th percentile1210
Maximum4820
Range4820
Interquartile range (IQR)610

Descriptive statistics

Standard deviation464.13723
Coefficient of variation (CV)1.4872307
Kurtosis4.08238
Mean312.08152
Median Absolute Deviation (MAD)0
Skewness1.6427322
Sum1435575
Variance215423.37
MonotonicityNot monotonic
2024-12-01T15:18:39.737911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2745
59.7%
500 53
 
1.2%
600 45
 
1.0%
800 43
 
0.9%
900 41
 
0.9%
700 38
 
0.8%
1000 33
 
0.7%
400 33
 
0.7%
550 27
 
0.6%
750 26
 
0.6%
Other values (197) 1516
33.0%
ValueCountFrequency (%)
0 2745
59.7%
20 1
 
< 0.1%
50 1
 
< 0.1%
60 2
 
< 0.1%
65 1
 
< 0.1%
70 1
 
< 0.1%
80 3
 
0.1%
90 2
 
< 0.1%
100 14
 
0.3%
110 2
 
< 0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
2850 1
< 0.1%
2730 1
< 0.1%
2550 2
< 0.1%
2360 1
< 0.1%
2330 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2180 1
< 0.1%

yr_built
Real number (ℝ)

High correlation 

Distinct115
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.7863
Minimum1900
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:39.896160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1913
Q11951
median1976
Q31997
95-th percentile2009
Maximum2014
Range114
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.731848
Coefficient of variation (CV)0.015086287
Kurtosis-0.6700759
Mean1970.7863
Median Absolute Deviation (MAD)23
Skewness-0.50215519
Sum9065617
Variance883.98281
MonotonicityNot monotonic
2024-12-01T15:18:40.072439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 111
 
2.4%
2005 104
 
2.3%
2007 93
 
2.0%
2004 92
 
2.0%
1978 90
 
2.0%
2008 89
 
1.9%
2003 89
 
1.9%
1967 82
 
1.8%
1977 80
 
1.7%
2014 78
 
1.7%
Other values (105) 3692
80.3%
ValueCountFrequency (%)
1900 22
0.5%
1901 9
 
0.2%
1902 10
 
0.2%
1903 10
 
0.2%
1904 9
 
0.2%
1905 19
0.4%
1906 27
0.6%
1907 12
0.3%
1908 19
0.4%
1909 22
0.5%
ValueCountFrequency (%)
2014 78
1.7%
2013 57
1.2%
2012 33
 
0.7%
2011 24
 
0.5%
2010 28
 
0.6%
2009 50
1.1%
2008 89
1.9%
2007 93
2.0%
2006 111
2.4%
2005 104
2.3%

yr_renovated
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean808.60826
Minimum0
Maximum2014
Zeros2735
Zeros (%)59.5%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:40.237238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31999
95-th percentile2011
Maximum2014
Range2014
Interquartile range (IQR)1999

Descriptive statistics

Standard deviation979.41454
Coefficient of variation (CV)1.2112349
Kurtosis-1.8511109
Mean808.60826
Median Absolute Deviation (MAD)0
Skewness0.3859187
Sum3719598
Variance959252.83
MonotonicityNot monotonic
2024-12-01T15:18:40.658760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2735
59.5%
2000 170
 
3.7%
2003 151
 
3.3%
2009 109
 
2.4%
2001 109
 
2.4%
2005 95
 
2.1%
2004 77
 
1.7%
2014 72
 
1.6%
2006 68
 
1.5%
2013 61
 
1.3%
Other values (50) 953
 
20.7%
ValueCountFrequency (%)
0 2735
59.5%
1912 33
 
0.7%
1913 1
 
< 0.1%
1923 57
 
1.2%
1934 6
 
0.1%
1945 7
 
0.2%
1948 1
 
< 0.1%
1953 1
 
< 0.1%
1954 8
 
0.2%
1955 2
 
< 0.1%
ValueCountFrequency (%)
2014 72
1.6%
2013 61
1.3%
2012 45
1.0%
2011 54
1.2%
2010 30
 
0.7%
2009 109
2.4%
2008 45
1.0%
2007 7
 
0.2%
2006 68
1.5%
2005 95
2.1%

Age
Real number (ℝ)

High correlation 

Distinct115
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.213696
Minimum10
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:40.827914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q127
median48
Q373
95-th percentile111
Maximum124
Range114
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.731848
Coefficient of variation (CV)0.55872549
Kurtosis-0.6700759
Mean53.213696
Median Absolute Deviation (MAD)23
Skewness0.50215519
Sum244783
Variance883.98281
MonotonicityNot monotonic
2024-12-01T15:18:40.993026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 111
 
2.4%
19 104
 
2.3%
17 93
 
2.0%
20 92
 
2.0%
46 90
 
2.0%
16 89
 
1.9%
21 89
 
1.9%
57 82
 
1.8%
47 80
 
1.7%
10 78
 
1.7%
Other values (105) 3692
80.3%
ValueCountFrequency (%)
10 78
1.7%
11 57
1.2%
12 33
 
0.7%
13 24
 
0.5%
14 28
 
0.6%
15 50
1.1%
16 89
1.9%
17 93
2.0%
18 111
2.4%
19 104
2.3%
ValueCountFrequency (%)
124 22
0.5%
123 9
 
0.2%
122 10
 
0.2%
121 10
 
0.2%
120 9
 
0.2%
119 19
0.4%
118 27
0.6%
117 12
0.3%
116 19
0.4%
115 22
0.5%

Total_sqft
Real number (ℝ)

High correlation 

Distinct659
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2451.4285
Minimum370
Maximum17670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2024-12-01T15:18:41.149688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile950
Q11547.5
median2260
Q33042.5
95-th percentile4610.5
Maximum17670
Range17300
Interquartile range (IQR)1495

Descriptive statistics

Standard deviation1242.1942
Coefficient of variation (CV)0.50672261
Kurtosis10.006532
Mean2451.4285
Median Absolute Deviation (MAD)750
Skewness1.8799238
Sum11276571
Variance1543046.5
MonotonicityNot monotonic
2024-12-01T15:18:41.320954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1150 28
 
0.6%
1480 28
 
0.6%
1010 26
 
0.6%
1940 26
 
0.6%
2120 24
 
0.5%
2550 24
 
0.5%
1700 23
 
0.5%
2670 23
 
0.5%
1200 23
 
0.5%
1590 23
 
0.5%
Other values (649) 4352
94.6%
ValueCountFrequency (%)
370 1
< 0.1%
380 1
< 0.1%
420 1
< 0.1%
430 1
< 0.1%
490 1
< 0.1%
520 1
< 0.1%
550 1
< 0.1%
560 1
< 0.1%
580 1
< 0.1%
590 2
< 0.1%
ValueCountFrequency (%)
17670 1
< 0.1%
14460 1
< 0.1%
12400 1
< 0.1%
11220 1
< 0.1%
9780 1
< 0.1%
9040 1
< 0.1%
8980 1
< 0.1%
8630 1
< 0.1%
8550 1
< 0.1%
8330 1
< 0.1%

Interactions

2024-12-01T15:18:34.432250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:17.986360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:19.366821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.834688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:22.884479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.310908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:25.679626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:27.043239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:28.679781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.060169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:31.440696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.888122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:34.549802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:18.094494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:19.501961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.967758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.002078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.420815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:25.788194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:27.150868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:28.795351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.172274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:31.560837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.999713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:34.661333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:18.202057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:19.601591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:21.082892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.111672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.526413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:25.891788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:27.494319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:28.898935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.278915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:31.671206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:33.102354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:34.786584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:18.329576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:19.733139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:21.227451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.239285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.656686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.013421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:27.648912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.032006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.401476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:31.800377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:33.223756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:34.913145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:18.452622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:19.872168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:21.924058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.362918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.779690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.131789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:27.764503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.153795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.524801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:31.927931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:33.539001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:35.031691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:18.565187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.000688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:22.042569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.480132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.886361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.245412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:27.874362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.266466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.636171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.049190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:33.649596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:35.148191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:18.675752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.127282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:22.159237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.594755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.997032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.353034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:27.983797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.378676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.748353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.166314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:33.758759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:35.258790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:18.781373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.238766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:22.271445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.704310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:25.102588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.458601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:28.082421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.483270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.854870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.278488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:33.864650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:35.378043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:18.894969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.352456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:22.398024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.820017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:25.214682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.567192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:28.187896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.592859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:30.966394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.396735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:33.972790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:35.495247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:19.009564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.460007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:22.517953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:23.936574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:25.325274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.677921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:28.334458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.704409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:31.076156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.515869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:34.083865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:35.635658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:19.134448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.587499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:22.645618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.065172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:25.450763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.811812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:28.453970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.829036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:31.209100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.644390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:34.206685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:35.753884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:19.244605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:20.698125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:22.761334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:24.184282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:25.560709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:26.921720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:28.563573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:29.939606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:31.319138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:32.761957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T15:18:34.313282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-01T15:18:41.449582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeTotal_sqftbathroomsbedroomsconditionfloorspricesqft_abovesqft_basementsqft_livingsqft_lotviewwaterfrontyr_builtyr_renovated
Age1.000-0.191-0.530-0.1600.265-0.538-0.084-0.4600.212-0.3220.0120.0530.027-1.0000.315
Total_sqft-0.1911.0000.6750.6310.0300.2170.6020.6280.5930.9430.2880.2100.2350.191-0.085
bathrooms-0.5300.6751.0000.5380.1290.5400.4920.6960.1900.7470.0920.1460.1660.530-0.213
bedrooms-0.1600.6310.5381.0000.0660.2200.3380.5330.2480.6520.2380.0860.0000.160-0.056
condition0.2650.0300.1290.0661.0000.1860.0000.1080.1170.0460.0520.0270.0000.2650.217
floors-0.5380.2170.5400.2200.1861.0000.3210.604-0.2880.397-0.2040.0330.0000.538-0.229
price-0.0840.6020.4920.3380.0000.3211.0000.5340.2370.6310.0750.0940.2260.084-0.071
sqft_above-0.4600.6280.6960.5330.1080.6040.5341.000-0.1720.8430.3050.1020.1340.460-0.169
sqft_basement0.2120.5930.1900.2480.117-0.2880.237-0.1721.0000.3230.0230.1950.211-0.2120.054
sqft_living-0.3220.9430.7470.6520.0460.3970.6310.8430.3231.0000.3250.1730.2690.322-0.127
sqft_lot0.0120.2880.0920.2380.052-0.2040.0750.3050.0230.3251.0000.0490.000-0.0120.051
view0.0530.2100.1460.0860.0270.0330.0940.1020.1950.1730.0491.0000.4830.0550.050
waterfront0.0270.2350.1660.0000.0000.0000.2260.1340.2110.2690.0000.4831.0000.0260.000
yr_built-1.0000.1910.5300.1600.2650.5380.0840.460-0.2120.322-0.0120.0550.0261.000-0.315
yr_renovated0.315-0.085-0.213-0.0560.217-0.229-0.071-0.1690.054-0.1270.0510.0500.000-0.3151.000

Missing values

2024-12-01T15:18:35.933028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-01T15:18:36.205770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedAgeTotal_sqft
02014-05-02 00:00:00313000.03.01.50134079121.50031340019552005691340
12014-05-02 00:00:002384000.05.02.50365090502.00453370280192101033930
22014-05-02 00:00:00342000.03.02.001930119471.00041930019660581930
32014-05-02 00:00:00420000.03.02.25200080301.00041000100019630613000
42014-05-02 00:00:00550000.04.02.501940105001.0004114080019761992482740
52014-05-02 00:00:00490000.02.01.0088063801.000388001938199486880
62014-05-02 00:00:00335000.02.02.00135025601.00031350019760481350
72014-05-02 00:00:00482000.04.02.502710358682.00032710019890352710
82014-05-02 00:00:00452500.03.02.502430884261.0004157086019850393290
92014-05-02 00:00:00640000.04.02.00152062001.50031520019452010791520
datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedAgeTotal_sqft
45902014-07-08 00:00:00380680.5555564.02.50262083312.00032620019910332620
45912014-07-08 00:00:00396166.6666673.01.75188057521.000494094019450792820
45922014-07-08 00:00:00252980.0000004.02.50253081692.00032530019930312530
45932014-07-08 00:00:00289373.3076923.02.50253846002.00032538020131923112538
45942014-07-09 00:00:00210614.2857143.02.50161072232.00031610019940301610
45952014-07-09 00:00:00308166.6666673.01.75151063601.00041510019541979701510
45962014-07-09 00:00:00534333.3333333.02.50146075732.00031460019832009411460
45972014-07-09 00:00:00416904.1666673.02.50301070142.00033010020090153010
45982014-07-10 00:00:00203400.0000004.02.00209066301.00031070102019740503110
45992014-07-10 00:00:00220600.0000003.02.50149081022.00041490019900341490